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In this article, a novel population selection method, fitness distance balance (FDB), and predictive candidate (PC) solution generation hybridization with starling murmuration optimizer (SMO), FDBPC-SMO are proposed. In FDBPC-SMO algorithm, FDB selects subpopulations instead of the separating search strategy (SSS) in the original SMO. The separating size determined in SMO is given as input to the FDB, and the FDB generates the subpopulation based on the distances among the populations. The least squares strategy is applied to the population obtained at the end of the SMO, and the estimated population candidates are found and replaced with the worst solution candidates from the original population. By adding qualitative analysis, the effectiveness of the FDBPC-SMO has been examined based on the dimension and iteration. The success of FDBPC-SMO is the selection of more efficient candidate solutions from the previous population at each iteration, thus minimizing the possibility of getting stuck in the local optimum. The performance of FDBPC-SMO has been investigated on CEC2017 and CEC2019 test sets and seven engineering application problems. In addition, Wilcoxon and Friedman statistical tests confirm the convergence and fitness results of the proposed method. Accordingly, comparing to conventional and improved methods, it is clear that the convergence ability of FDBPC-SMO is superior. [ABSTRACT FROM AUTHOR]

This paper develops a simple and computationally efficient parametric approach to the estimation of general hidden Markov models (HMMs). For non-Gaussian HMMs, the computation of the maximum likelihood estimator (MLE) involves a high-dimensional integral that has no analytical solution and can be difficult to approach accurately. We develop a new alternative method based on the theory of estimating functions and a deconvolution strategy. Our procedure requires the same assumptions as the MLE and deconvolution estimators. We provide theoretical guarantees about the performance of the resulting estimator; its consistency and asymptotic normality are established. This leads to the construction of confidence intervals. Monte Carlo experiments are investigated and compared with the MLE. Finally, we illustrate our approach using real data for ex-ante interest rate forecasts. [ABSTRACT FROM AUTHOR]

Accurate prediction of forest fire spread is very essential for minimizing its effects. Although many models have been developed to predict the forest fire spread, all these models require several parameters, sometimes, cannot be obtained in a real time. In this paper, the grey system theory was applied for forest fire spread model developing. By preprocessing and fusing the MODIS remote sensing data, the sequence data of the grey model can be confirmed. After making precision comparison among least square estimation algorithm, least square interpolation algorithm and ER algorithm, forest fire spread GM(1, 1) model was developed with ER algorithm and precision of the model was evaluated at the same time. The results showed that the fitting precision and predicting precision of the model were both high, of which the one-level model made up 50%, the two-level 25% and the model between the one-level and the two-level 25%. The prediction accuracy of forest fire spreading model was tested to meet the requirement of modeling. GM(1, 1) model provided a new approach for the study of forest fire spread simulation. [ABSTRACT FROM AUTHOR]

In this paper we derive the asymptotic properties of the least squares estimator (LSE) of autoregressive moving-average (ARMA) models with regime changes under the assumption that the errors are uncorrelated but not necessarily independent. Relaxing the independence assumption considerably extends the range of application of the class of ARMA models with regime changes. Conditions are given for the consistency and asymptotic normality of the LSE. A particular attention is given to the estimation of the asymptotic covariance matrix, which may be very different from that obtained in the standard framework. The theoretical results are illustrated by means of Monte Carlo experiments. [ABSTRACT FROM AUTHOR]

Accurate forecasting and scientific analysis of building electrical load can improve the level of building energy management to meet the requirements of energy saving. To further strengthen the forecasting accuracy, this study presents a hybrid model for building electrical load forecasting. The proposed method combines the fuzzy inference system and the periodicity knowledge together to generate accurate forecasting results. In this method, in order to better reflect the actual characteristic of the electrical load, the wavelet transform method is firstly utilized to filter the original building electrical load data. Then, the daily periodic pattern is extracted from such filtered electrical load data, and the residual data are obtained through removing the daily periodic pattern. Further, the residual data-driven forecasting model is constructed by the functionally weighted single-input-rule-modules connected fuzzy inference system (FWSIRM-FIS). This FWSIRM-FIS model is used to provide the compensation to the periodic component. In other words, the daily periodic component and the residual forecasting are combined to achieve the final forecasting result. Specifically, in order to assure the forecasting performance of the FWSIRM-FIS model, the subtraction clustering method is employed to construct the SIRMs while the least square estimation is utilized to optimize the parameters in the functional weights of the FWSIRM-FIS. Finally, in this paper, two real-world experiments are made and detailed comparisons with four traditional models are given. Experimental and comparison results demonstrate that the proposed hybrid model has the smallest forecasting errors and can achieve the best performance. [ABSTRACT FROM AUTHOR]

Benford's law (BL), also known as the first-digit or significant-digit law, is an intriguing pattern in data sets, considers the frequency of occurrence of the first digits, which are not uniformly distributed as might be expected, conversely follow a specified theoretical distribution. According to BL, the occurrence of first non-zero digit in a numerical data, which is generated or found in nature, depends on a logarithmic distribution. Least square estimation (LSE) method is mostly preferred for the estimation of the unknown parameters from different types of geodetic data. The residuals and the normalized residuals of the LSE method, which follow normal distribution and expected values of them are zero are used in outlier detection problem. In this study, BL is investigated for residuals and the normalized residuals estimated from LSE method. Three types of geodetic data are used: (1) simulated regression models, (2) global positioning system (GPS) data, (3) leveling network. The first group data sets are simulated based on linear regression and univariate models and each simulated group is generated for a number of 100, 1000, and 10,000 samples. To generate second group, an international global navigation satellite system (GNSS) service (IGS) station data (ISTA) is processed by kinematic PPP approach using GIPSY OASIS II v6.4 software. Here, the observation duration of GPS data is 4 days. For the last data, a leveling network with 55 points involving 110 observations of height differences is simulated. BL has been applied to the residuals (v) and normalized residuals (w) estimated from LSE method. Goodness-of-fit test has been implemented to determine whether a population has a specified BL distribution or not. This test is based on how good a fit we have between the frequency of occurrence of residuals and normalized residuals in an observed sample and the expected frequencies obtained from the hypothesized distribution. The results depending on the statistical test show that each data set (residuals and normalized residuals) used in this study follows BL. [ABSTRACT FROM AUTHOR]

In this paper, orthogonal moment invariant-based features are used to compute 2D optical flow from sequence of images. The gray level of pixel is described in terms of its local neighborhood using Tchebichef moment invariants rather than its individual intensity value. The description is then normalized in order to make it insensitive to intensity fluctuations due to noise or other perturbations such as varying illumination conditions. The principle of conservation of moment invariance is used to derive overdetermined system of 2D motion constraint equations in local neighborhood of each pixel. The velocity field is then estimated using the least square method. Experimental results are performed on sequential video and thermal image frames under varying environmental conditions. The run time, robustness against noisy and varying illumination conditions and rotation invariance of proposed method are compared with already existing moment and non-moment-based techniques. [ABSTRACT FROM AUTHOR]

The Nakagami distribution is widely used in communication engineering. In this article we consider this distribution as a useful life time model in life testing experiments and reliability theory. Some of its distributional properties and reliability characteristics are discussed. In order to reduce cost and time of life testing experiments progressive type II censoring is used. Maximum likelihood (ML) and least square estimators of the unknown parameters and reliability characteristics are derived with progressively type II censored sample from this distribution. Interval estimation and coverage probability based on ML estimates are obtained. Monte Carlo simulation study is performed to compare various estimates developed. Findings are illustrated by three examples, two based on simulated data sets and one consisting of a real data set. [ABSTRACT FROM AUTHOR]

In this article, the Bayes linear unbiased estimation (BALUE) of parameters is derived for the partitioned linear model. The superiorities of the BALUE over ordinary least square estimator (LSE) are studied in terms of the Bayes mean square error matrix (BMSEM) criterion and Pitman closeness (PC) criterion. [ABSTRACT FROM AUTHOR]

In this paper the limiting distribution of the least square estimate for the autoregressive coefficient of a nearly unit root model with GARCH errors is derived. Since the limiting distribution depends on the unknown variance of the errors, an empirical likelihood ratio statistic is proposed from which confidence intervals can be constructed for the nearly unit root model without knowing the variance. To gain an intuitive sense for the empirical likelihood ratio, a small simulation for the asymptotic distribution is given. [ABSTRACT FROM AUTHOR]

Abstract In target node localization problem, conventional methods based on received signal strength indicator (RSSI) assume a prior knowledge of a channel model and values of its parameters specific for an environment. This limits the conventional localization system to be set up quickly and effectively due to a necessary pre-measurement step to determine both the channel model and the values of its parameters. To address the limitation, a twostage iterative algorithm which allows to localize a target node without any prior knowledge of the parameter values has been propose. Each stage of the algorithm can be implemented using different estimation methods, such as maximum likelihood (ML) and least square (LS) estimation which provides four different combinations. To determine the best combination, the location estimation performance for all four combinations is evaluated using experimental data collected in measurement campaigns on various indoor locations. The results reveal that the combination of ML estimation method implemented in both stages provides the best location estimation accuracy and the fastest convergence rate. [ABSTRACT FROM AUTHOR]

Self-location is a major function of unmanned aerial vehicle (UAV). In the absence of satellite navigation signals, a UAV depending merely on a low-accuracy inertial measurement unit (IMU) certainly fails to meet the needs of self-location. This paper introduces a monocular vision-based self-location method of UAV just fitted with a single downward-looking camera. A self-location mathematic model is built in accordance with the collinear relationship among the ground target point, the image point and the optical center of the camera. By taking conformal geometric algebra (CGA) as a mathematic tool, direct operation for geometric elements and geometric relationship is performed along with efficient extension, unary elimination and simplification. Combined with the least square estimation, the spatial location of UAVs can be calculated. Finally, the performance of this approach is verified via a flight test, experimental results show that the UAV location can be obtained with this method, which also embraces significant advantages in the convergence rate and computing time. Furthermore, it can be deployed to embedded devices with limited computing resources, thus it meets the positive real-time requirements of self-location of UAVs. [ABSTRACT FROM AUTHOR]